Recently, Passos et al. [1]} proposed to employ metaheuristic approaches in the context of DBM hyperparameter optimization. However, the work deals only with Harmony Search [2]} and Particle Swarm Optimization [3]} techniques. Moreover, the paper presents a shallow discussion regarding the experimental results. Therefore, in this work, we considered DBM hyperparameter fine-tuning in the context of music-inspired, swarm-based and differential evolution algorithms, employing seven different techniques: Improved Harmonic-Search (IHS) [4]}, Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) [5]}, Cuckoo Search (CS) [6]}, Firefly Algorithm (FA) [7]}, Backtracking Search Optimization Algorithm (BSA) [8]}, Adaptive Differential Evolution (JADE) [9]}, and the Differential Evolution Based on Covariance Matrix Learning and Bimodal Distribution Parameter Setting Algorithm (CoBiDE) [10]}. Furthermore, all techniques are compared with a random search for experimental purposes. Additionally, this work provides a more detailed experimental section, considering a statistical similarity and time consumption comparison. Finally, the application addressed in this paper concerns the task of binary image reconstruction, and for that purpose, we considered three public datasets.
